Instructions to use prithivMLmods/Llama-3.2-8B-GGUF-200K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Llama-3.2-8B-GGUF-200K")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Llama-3.2-8B-GGUF-200K", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

Use Docker

docker model run hf.co/prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

LM Studio
Jan

vLLM

How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Llama-3.2-8B-GGUF-200K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama-3.2-8B-GGUF-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

SGLang

How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Llama-3.2-8B-GGUF-200K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama-3.2-8B-GGUF-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Llama-3.2-8B-GGUF-200K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama-3.2-8B-GGUF-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with Ollama:
```
ollama run hf.co/prithivMLmods/Llama-3.2-8B-GGUF-200K:F16
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Llama-3.2-8B-GGUF-200K:F16
```

Lemonade

How to use prithivMLmods/Llama-3.2-8B-GGUF-200K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Llama-3.2-8B-GGUF-200K:F16

Run and chat with the model

lemonade run user.Llama-3.2-8B-GGUF-200K-F16

List all available models

lemonade list

Atomic Chat

Llama-3.2-8B-GGUF-200K

Attribute	Description
Developed by	prithivMLmods
License	Apache-2.0
Finetuned from model	unsloth/meta-llama-3.1-8b-bnb-4bit

Run with Ollama 🦙

Download and Install Ollama

To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Run Your Own Model in Minutes

Steps to Run GGUF Models:

1. Create the Model File

Name your model file appropriately, for example, metallama200.

2. Add the Template Command

Include a FROM line with the base model file. For instance:
```
FROM Llama-3.2-8B-GGUF-200K
```
Make sure the model file is in the same directory as your script.

3. Create and Patch the Model

Use the following command in your terminal to create and patch your model:
```
ollama create metallama200 -f ./metallama200
```
Upon success, a confirmation message will appear.
To verify that the model was created successfully, run:
```
ollama list
```
Ensure that metallama200 appears in the list of models.

Running the Model

To run the model, use:

ollama run metallama200

Sample Usage

In the command prompt, run:

D:\>ollama run metallama200

Example interaction:

>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.

Downloads last month: 1,175

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit

Dataset used to train prithivMLmods/Llama-3.2-8B-GGUF-200K

Collection including prithivMLmods/Llama-3.2-8B-GGUF-200K

GPT-Generated Unified Format (GGUF)

Collection

ease of reading • 53 items • Updated 1 day ago • 4